We present an integrated approach by combining analog computing and deep
learning for electrocardiogram (ECG) arrhythmia classification. We propose
EKGNet, a hardware-efficient and fully analog arrhythmia classification
architecture that archives high accuracy with low power consumption. The
proposed architecture leverages the energy efficiency of transistors operating
in the subthreshold region, eliminating the need for analog-to-digital
converters (ADC) and static random access memory (SRAM). The system design
includes a novel analog sequential Multiply-Accumulate (MAC) circuit that
mitigates process, supply voltage, and temperature variations. Experimental
evaluations on PhysioNet's MIT-BIH and PTB Diagnostics datasets demonstrate the
effectiveness of the proposed method, achieving average balanced accuracy of
95% and 94.25% for intra-patient arrhythmia classification and myocardial
infarction (MI) classification, respectively. This innovative approach presents
a promising avenue for developing low-power arrhythmia classification systems
with enhanced accuracy and transferability in biomedical applications.

我们提出了一种整合模拟计算和深度学习的方法用于心电图（ECG）心律失常分类。我们提出了 EKGNet，一种硬件高效且完全模拟的心律失常分类架构，其在低功耗下实现了高准确性。该架构利用亚阈值区操作的晶体管的能量效率，消除了模数转换器（ADC）和静态随机存取内存（SRAM）的需求。系统设计包括一种新颖的模拟顺序乘积累加（MAC）电路，可以减轻过程、供电电压和温度的变化。对 PhysioNet 的 MIT-BIH 和 PTB 诊断数据集的实验评估表明，该方法的有效性，分别实现了 95% 和 94.25% 的平均平衡准确性，用于患者内心律失常分类和心肌梗死（MI）分类。这种创新的方法为开发低功耗心律失常分类系统在生物医学应用中提供了一个有前景的途径。